Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912, and Department of Psychology, University of New Mexico, Albuquerque, New Mexico 87131.

Abstract

Human cognition is flexible and adaptive, affording the ability to detect and leverage complex structure inherent in the environment and generalize this structure to novel situations. Behavioral studies show that humans impute structure into simple learning problems, even when this tendency affords no behavioral advantage. Here we used electroencephalography to investigate the neural dynamics indicative of such incidental latent structure. Event-related potentials over lateral prefrontal cortex, typically observed for instructed task rules, were stratified according to individual participants' constructed rule sets. Moreover, this individualized latent rule structure could be independently decoded from multielectrode pattern classification. Both neural markers were predictive of participants' ability to subsequently generalize rule structure to new contexts. These EEG dynamics reveal that the human brain spontaneously constructs hierarchically structured representations during learning of simple task rules.

Experimental protocol. a, Task design. In the learning phase, subjects learned to pick one of 4 actions for 4 different colored shapes. During the asymptotic learning phase, RT switch costs (switch minus stay RTs) were computed online for the color or shape dimension (ΔC and ΔS, respectively). If ΔC > ΔS, color was treated as context (high dimension [H]), and shape was stimulus (low dimension [L]); otherwise, their roles were reversed. In the transfer phase, subjects learned four new inputs from the previous stimuli (shapes or color), but with new contexts (rules) H3 and H4. H3 tested positive transfer of an old TS (TS1), whereas H4 tested negative transfer for learning a new TS (TS3). b, Temporal structure of the task. Subjects saw a colored shape and were required to press one key within 1.5 s. Responses were followed by deterministic audiovisual feedback. Three identically structured blocks of the task were completed. Each block comprised a learning phase followed (without interruption) by the transfer phase. Asymptotic learning phases were defined as the last 40 trials of the learning phase, occurring after subjects reached a learning criterion.

Behavioral results. a, b, Model predictions from computational model described previously (). c, d, Behavioral data, whole group (N = 35). a, c, Positive transfer is evident by enhanced learning curves for H3 context in which it was possible to generalize the TS TS1 learned previously in context H1, compared with H4, associated with a new TS. Black lines indicate within-subject performance differences between contexts H3 and H4. b, d, Negative transfer is evident by the distribution of error types. Errors in H4 were more often associated with neglecting the high dimension (NH) rather than low dimension (NL), indicating that participants selected actions that would have been correct in H1 or H2, even though these rules were not applicable here. e, f, Individual differences were evident such that some participants exhibited very robust positive transfer (N = 19), whereas others showed none at all (N = 16). Error bars indicate SEM. * indicates p < 0.05.

Neural network activation shows differential early and late switch H effects and no switch L effects. a, Sketch of the neural network described by ). TSs are selected in the more anterior part of prefrontal cortex (labeled here PFC)–basal ganglia (BG) loop in response to a context (C). Motor actions (M) are chosen in a more posterior PFC (or PMC)–BG loop in response to TS-contextualized stimuli S. b, Anterior PFC layer shows early, H-dimension-specific switch-related increase in activation. This layer is related to the cue-TS selection process. c, Posterior prefrontal (PMC) layer shows late, H-dimension-specific switch-related increase in activation. This layer only becomes active in the presence of a target (or stimulus) after the target is presented.

Early positivity effects in anterior versus posterior rdlPFC electrodes. Topoplot on left, Position of the a posteriori rdlPFC ROIs. The scatter plots show that the amplitude difference of Switch-H versus Stay-H, a marker of hierarchical task structure during learning, is correlated with subsequent positive transfer in the posterior part of rdlPFC ROI (left) and anterior part rdlPFC ROI (middle). The correlation is stronger in anterior than posterior electrodes (right).

Classification results (N = 32). a, Localizer task. Subjects saw 8 blocks of stimuli changing on a single dimension, either color or shape. These localizers were used to train the pattern classifier to identify neural indices indicative of attention to color or shape and were subsequently applied to classify activity during the asymptotic learning phase of the structure learning task. b, Classifier performance for localizer color and shape trials. c, e, g, Percentage classification as high dimension across the whole group of subjects (c), and separately in those who exhibited little evidence for positive transfer (e), and those showing substantial evidence for positive transfer (g). Groups are the same as in c, d. d, f, Scatterplots represent the correlation between classification performance and positive transfer (d) or Switch-H-related early positivity (bottom). Error bars indicate SEM. b, Open circles represent significantly higher classification than prebaseline (p = 0.05, uncorrected). c, g, **Significant classification toward H over the relevant time interval (p < 0.01). Red asterisk indicates post hoc tests of significance at each time point (p < 0.05); + p = 0.07.

Indirect observations of latent TS structure. Although hierarchical TS structure is latent, and hence directly unobservable, converging indicators of such structure are evident in behavior and dynamic EEG activity. These indices are correlated with each other (full gray arrows), suggesting that they reflect the same underlying construct. Similarly, these measures exhibit an asymmetric role of high and low dimensions (dotted gray arrows).